JOSS submission analytics
2020-09-27 19:58:54
- Introduction
- Load required R packages
- Collect information about papers
- Tabulate number of missing values
- Number of published papers per month and year
- Citation distribution
- Most cited papers
- Citation count vs time since publication
- Power law of citation count within each half year
- Pre-review/review time over time
- Languages
- Association between number of citations and number of stars of the GitHub repo
- Distribution of time between GitHub repo creation and JOSS submission
- Distribution of time between JOSS acceptance and last commit
- Number of authors per paper
- Number of authors vs number of contributors to the GitHub repo
- Number of reviewers per paper
- Most active reviewers
- Number of papers per editor and year
- Distribution of software repo licenses
- Most common GitHub repo topics (work in progress)
- Citation analysis
- Save object
- Session info
Introduction
In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.
Load required R packages
suppressPackageStartupMessages({
library(tibble)
library(rcrossref)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(gh)
library(purrr)
library(jsonlite)
library(DT)
library(plotly)
library(citecorp)
library(readr)
})## Keep track of the source of each column
source_track <- c()
## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
dcap <- lubridate::today()
} else {
dcap <- ""
}## Read archived version of summary data frame, to use for filling in
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)
## Similarly for citation analysis, to avoid having to pull down the
## same information multiple times
citations_archive <- readr::read_delim(
url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
col_types = cols(.default = "c"), col_names = TRUE,
delim = "\t")Collect information about papers
Pull down papers and citation info from Crossref
We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.
## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim)$data## Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
## Please use `tibble::as_tibble()` instead.
## [90mThis warning is displayed once every 8 hours.[39m
## [90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39mi <- 1
while (nrow(papers) == i * lim) {
papers <- dplyr::bind_rows(
papers,
rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim, offset = i * lim)$data)
i <- i + 1
}
papers <- papers %>%
dplyr::filter(type == "journal-article")
## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
papers$url[noaltid])
## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
cit %>% dplyr::rename(citation_count = count),
by = c("alternative.id" = "doi")
)
## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")
source_track <- c(source_track,
structure(rep("crossref", ncol(papers)),
names = colnames(papers)))Pull down info from Whedon API
For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.
whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0) {
whedon <- c(whedon, a)
p <- p + 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
}
whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
data.frame(api_title = w$title,
api_state = w$state,
editor = paste(w$metadata$paper$editor, collapse = ","),
reviewers = paste(w$reviewers, collapse = ","),
nbr_reviewers = length(w$reviewers),
repo_url = w$repository_url,
review_issue_id = w$review_issue_id,
doi = w$doi,
prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
w$meta_review_issue_id, NA_integer_),
languages = paste(w$metadata$paper$languages, collapse = ","),
archive_doi = w$metadata$paper$archive_doi)
}))
papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))
source_track <- c(source_track,
structure(rep("whedon", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Combine with info from GitHub issues
From each pre-review and review issue, we extract information about review times and assigned labels.
## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues",
.limit = 5000, state = "all")## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
data.frame(title = i$title,
number = i$number,
state = i$state,
opened = i$created_at,
closed = ifelse(!is.null(i$closed_at),
i$closed_at, NA_character_),
ncomments = i$comments,
labels = paste(setdiff(
vapply(i$labels, getElement,
name = "name", character(1L)),
c("review", "pre-review", "query-scope", "paused")),
collapse = ","))
}))
## Split into REVIEW, PRE-REVIEW, and other issues (the latter category
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) &
!grepl("\\[REVIEW\\]", title))
dim(issother)## [1] 27 7## title
## 1 @TheoChristiaanse Thanks for your submission! A very quick initial comment is that was not straightforward for me to:
## 2 @torressa @poulson I only found a couple of small issues:
## 3 Request to regenerate final proof
## 4 issues running example program Karate
## 5 @whedon commands
## 6 @whedon
## number state opened closed ncomments labels
## 1 2652 closed 2020-09-08T16:33:13Z 2020-09-08T16:48:16Z 3
## 2 2082 closed 2020-02-07T09:51:50Z 2020-02-07T09:52:09Z 2
## 3 2045 closed 2020-01-28T14:44:07Z 2020-01-28T14:45:26Z 2
## 4 2015 closed 2020-01-15T13:25:37Z 2020-01-15T15:05:18Z 3
## 5 1898 closed 2019-11-17T09:44:23Z 2019-11-17T10:26:41Z 4
## 6 1897 closed 2019-11-17T09:43:49Z 2019-11-17T10:26:30Z 4## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
dplyr::mutate(alternative.id = paste0("10.21105/joss.",
nbrzeros,
number)) %>%
dplyr::select(-nbrzeros) %>%
dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))## Tabulate the number of pre-review issues labeled 'rejected' per year
iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(grepl("rejected", labels)) %>%
dplyr::mutate(year = lubridate::year(opened)) %>%
dplyr::group_by(year) %>%
dplyr::summarize(nbr_rejected = length(labels))## [90m# A tibble: 4 x 2[39m
## year nbr_rejected
## [3m[90m<dbl>[39m[23m [3m[90m<int>[39m[23m
## [90m1[39m [4m2[24m017 6
## [90m2[39m [4m2[24m018 16
## [90m3[39m [4m2[24m019 14
## [90m4[39m [4m2[24m020 88## For PRE-REVIEW issues, add information about the corresponding REVIEW
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!grepl("withdrawn", labels)) %>%
dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>%
dplyr::filter(!duplicated(title)) %>%
dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
dplyr::rename_all(~ paste0("prerev_", .))
papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>%
dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
dplyr::mutate(prerev_opened = as.Date(prerev_opened),
prerev_closed = as.Date(prerev_closed),
review_opened = as.Date(review_opened),
review_closed = as.Date(review_closed)) %>%
dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
days_in_rev = review_closed - review_opened,
to_review = !is.na(review_opened))
source_track <- c(source_track,
structure(rep("joss-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Add information from software repositories
## Reorder so that software repositories that were interrogated longest
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)## [1] 1032## [1] 987## [1] "https://bitbucket.org/cmutel/brightway2"
## [2] "https://bitbucket.org/cloopsy/android/"
## [3] "https://bitbucket.org/manuela_s/hcp/"
## [4] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [5] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [6] "https://bitbucket.org/glotzer/rowan"
## [7] "https://gitlab.com/moorepants/skijumpdesign"
## [8] "https://gitlab.com/toposens/public/ros-packages"
## [9] "https://gitlab.inria.fr/azais/treex"
## [10] "https://bitbucket.org/basicsums/basicsums"
## [11] "https://savannah.nongnu.org/projects/complot/"
## [12] "http://mutabit.com/repos.fossil/grafoscopio/"
## [13] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [14] "https://bitbucket.org/cdegroot/wediff"
## [15] "https://gitlab.com/materials-modeling/wulffpack"
## [16] "https://gitlab.com/costrouc/pysrim"
## [17] "https://bitbucket.org/meg/cbcbeat"
## [18] "https://vcs.ynic.york.ac.uk/analysis/sails"
## [19] "https://bitbucket.org/ocellarisproject/ocellaris"
## [20] "https://gitlab.com/QComms/cqptoolkit"
## [21] "https://gitlab.com/dlr-dw/ontocode"
## [22] "https://gitlab.com/eidheim/Simple-Web-Server"
## [23] "https://bitbucket.org/dghoshal/frieda"
## [24] "https://gitlab.com/tesch1/cppduals"
## [25] "https://gitlab.com/gdetor/genetic_alg"
## [26] "https://bitbucket.org/hammurabicode/hamx"
## [27] "https://gitlab.com/datafold-dev/datafold/"
## [28] "https://bitbucket.org/likask/mofem-cephas"
## [29] "https://www.idpoisson.fr/fullswof/"
## [30] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [31] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [32] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [33] "https://c4science.ch/source/tamaas/"
## [34] "https://gitlab.inria.fr/miet/miet"
## [35] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [36] "https://gitlab.com/myqueue/myqueue"
## [37] "https://gitlab.com/cerfacs/batman"
## [38] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [39] "https://ts-gitlab.iup.uni-heidelberg.de/dorie/dorie"
## [40] "https://gitlab.com/davidtourigny/dynamic-fba"
## [41] "https://gitlab.com/celliern/scikit-fdiff/"
## [42] "https://gitlab.com/ampere2/metalwalls"
## [43] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/utopia"
## [44] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/dantro"
## [45] "https://gitlab.com/cosmograil/PyCS3"df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
if (grepl("/tree/", u0)) {
u0 <- strsplit(u0, "/tree/")[[1]][1]
}
if (grepl("/blob/", u0)) {
u0 <- strsplit(u0, "/blob/")[[1]][1]
}
info <- try({
gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
})
languages <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"),
.limit = 500)
})
topics <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"),
.accept = "application/vnd.github.mercy-preview+json", .limit = 500)
})
contribs <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"),
.limit = 500)
})
if (!is(info, "try-error") && length(info) > 1) {
if (!is(contribs, "try-error")) {
if (length(contribs) == 0) {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
} else {
repo_nbr_contribs <- length(contribs)
repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
if (is.na(repo_nbr_contribs_2ormore)) {
print(contribs)
}
}
} else {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
}
if (!is(languages, "try-error")) {
if (length(languages) == 0) {
repolang <- ""
} else {
repolang <- paste(paste(names(unlist(languages)),
unlist(languages), sep = ":"), collapse = ",")
}
} else {
repolang <- ""
}
if (!is(topics, "try-error")) {
if (length(topics$names) == 0) {
repotopics <- ""
} else {
repotopics <- paste(unlist(topics$names), collapse = ",")
}
} else {
repotopics <- ""
}
data.frame(repo_url = u,
repo_created = info$created_at,
repo_updated = info$updated_at,
repo_pushed = info$pushed_at,
repo_nbr_stars = info$stargazers_count,
repo_language = ifelse(!is.null(info$language),
info$language, NA_character_),
repo_languages_bytes = repolang,
repo_topics = repotopics,
repo_license = ifelse(!is.null(info$license),
info$license$key, NA_character_),
repo_nbr_contribs = repo_nbr_contribs,
repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
)
} else {
NULL
}
})) %>%
dplyr::mutate(repo_created = as.Date(repo_created),
repo_updated = as.Date(repo_updated),
repo_pushed = as.Date(repo_pushed)) %>%
dplyr::distinct() %>%
dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)
## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)
papers <- papers %>% dplyr::left_join(df, by = "repo_url")
source_track <- c(source_track,
structure(rep("sw-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Clean up a bit
## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
dplyr::mutate(published.date = as.Date(published.print)) %>%
dplyr::mutate(
halfyear = paste0(year(published.date),
ifelse(month(published.date) <= 6, "H1", "H2"))
) %>% dplyr::mutate(
halfyear = factor(halfyear,
levels = paste0(rep(sort(unique(year(published.date))),
each = 2), c("H1", "H2")))
) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
papers <- papers %>% dplyr::distinct()
source_track <- c(source_track,
structure(rep("cleanup", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))Tabulate number of missing values
In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.
Number of published papers per month and year
ggplot(papers %>%
dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
dplyr::group_by(pubmonth) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubmonth), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per month", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
ggplot(papers %>%
dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
dplyr::group_by(pubyear) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubyear), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per year", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Citation distribution
Papers with 20 or more citations are grouped in the “>=20” category.
ggplot(papers %>%
dplyr::mutate(citation_count = replace(citation_count,
citation_count >= 20, ">=20")) %>%
dplyr::mutate(citation_count = factor(citation_count,
levels = c(0:20, ">=20"))) %>%
dplyr::group_by(citation_count) %>%
dplyr::tally(),
aes(x = citation_count, y = n)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(x = "Crossref citation count", y = "Number of publications", caption = dcap)
Most cited papers
The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.
DT::datatable(
papers %>%
dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>",
url,"</a>")) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::select(title, url, published.date, citation_count),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Citation count vs time since publication
plotly::ggplotly(
ggplot(papers, aes(x = published.date, y = citation_count, label = title)) +
geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() +
geom_smooth() +
labs(x = "Date of publication", y = "Crossref citation count", caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)Power law of citation count within each half year
Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.
ggplot(papers %>% dplyr::group_by(halfyear) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::mutate(idx = seq_along(citation_count)),
aes(x = idx, y = citation_count)) +
geom_point(alpha = 0.5) +
facet_wrap(~ halfyear, scales = "free") +
theme_bw() +
labs(x = "Index", y = "Crossref citation count", caption = dcap)
Pre-review/review time over time
In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.
ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review",
caption = dcap) +
theme(axis.title = element_text(size = 15))
ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of review opening", y = "Number of days in review",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Languages
Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.
## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
data.frame(language = l,
nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>%
setNames(c("language", "bytes")) %>%
dplyr::mutate(bytes = as.numeric(bytes)) %>%
dplyr::filter(!is.na(language)) %>%
dplyr::group_by(language) %>%
dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
nbr_repos_GitHub = length(bytes)) %>%
dplyr::arrange(desc(nbr_bytes_GitHub))
langs <- dplyr::full_join(langs, langbytes, by = "language")ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
dplyr::filter(nbr_submissions_Whedon > 10) %>%
dplyr::mutate(language = factor(language, levels = language)),
aes(x = language, y = nbr_submissions_Whedon)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15))
DT::datatable(
langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) +
geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() +
theme_bw() +
labs(x = "Number of repos using the language",
y = "Total number of bytes of code\nwritten in the language",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Association between number of citations and number of stars of the GitHub repo
ggplotly(
ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
label = title)) +
geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() +
theme_bw() +
labs(x = "Crossref citation count", y = "Number of stars, GitHub repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)Distribution of time between GitHub repo creation and JOSS submission
ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from repo creation to JOSS pre-review start",
caption = dcap) +
theme(axis.title = element_text(size = 15))
Distribution of time between JOSS acceptance and last commit
ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)) +
facet_wrap(~ year(published.date), scales = "free_y")
Number of authors per paper
List the papers with the largest number of authors, and display the distribution of the number of authors per paper, for papers with at most 20 authors.
## Papers with largest number of authors
papers %>% dplyr::arrange(desc(nbr_authors)) %>%
dplyr::select(title, published.date, url, nbr_authors) %>%
as.data.frame() %>% head(10)## title
## 1 SunPy: A Python package for Solar Physics
## 2 ENZO: An Adaptive Mesh Refinement Code for Astrophysics (Version 2.6)
## 3 PyBIDS: Python tools for BIDS datasets
## 4 Chaste: Cancer, Heart and Soft Tissue Environment
## 5 spam: Software for Practical Analysis of Materials
## 6 VIVO: a system for research discovery
## 7 Welcome to the Tidyverse
## 8 Pyglmnet: Python implementation of elastic-net regularized generalized linear models
## 9 HPX - The C++ Standard Library for Parallelism and Concurrency
## 10 AMReX: a framework for block-structured adaptive mesh refinement
## published.date url nbr_authors
## 1 2020-02-14 http://dx.doi.org/10.21105/joss.01832 124
## 2 2019-10-03 http://dx.doi.org/10.21105/joss.01636 55
## 3 2019-08-12 http://dx.doi.org/10.21105/joss.01294 31
## 4 2020-03-13 http://dx.doi.org/10.21105/joss.01848 29
## 5 2020-07-13 http://dx.doi.org/10.21105/joss.02286 27
## 6 2019-07-26 http://dx.doi.org/10.21105/joss.01182 25
## 7 2019-11-21 http://dx.doi.org/10.21105/joss.01686 24
## 8 2020-03-01 http://dx.doi.org/10.21105/joss.01959 22
## 9 2020-09-02 http://dx.doi.org/10.21105/joss.02352 19
## 10 2019-05-12 http://dx.doi.org/10.21105/joss.01370 17nbins <- max(papers$nbr_authors[papers$nbr_authors <= 20])
ggplot(papers %>% dplyr::filter(nbr_authors <= 20),
aes(x = nbr_authors)) +
geom_histogram(bins = nbins, fill = "lightgrey", color = "grey50") +
theme_bw() +
facet_wrap(~ year(published.date), scales = "free_y") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Number of authors",
y = "Number of publications with\na given number of authors",
caption = dcap)
ggplot(papers %>%
dplyr::mutate(nbr_authors = replace(nbr_authors, nbr_authors > 5, ">5")) %>%
dplyr::mutate(nbr_authors = factor(nbr_authors, levels = c("1", "2", "3",
"4", "5", ">5"))) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(year = factor(year)) %>%
dplyr::group_by(year, nbr_authors, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = nbr_authors)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Number of\nauthors",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)
Number of reviewers per paper
Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
ggplot(papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)),
aes(x = nbr_reviewers)) + geom_bar() +
facet_wrap(~ year) + theme_bw() +
labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)
Most active reviewers
Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)Number of papers per editor and year
ggplot(papers %>%
dplyr::mutate(year = year(published.date),
`r/pyOpenSci` = factor(
grepl("rOpenSci|pyOpenSci", prerev_labels),
levels = c("TRUE", "FALSE"))),
aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) +
theme_bw() + facet_wrap(~ year, ncol = 1) +
scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Editor", y = "Number of submissions", caption = dcap)
Distribution of software repo licenses
all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
grep("bsd", all_licenses, value = TRUE),
grep("mit", all_licenses, value = TRUE),
grep("gpl", all_licenses, value = TRUE),
grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>%
dplyr::mutate(repo_license = factor(repo_license,
levels = license_levels)),
aes(x = repo_license)) +
geom_bar() +
theme_bw() +
labs(x = "Software license", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~ year(published.date), scales = "free_y")
## For plots below, replace licenses present in less
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::count() %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = n, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Number of submissions", caption = dcap)
ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)
Most common GitHub repo topics (work in progress)
a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)
colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE,
colors = colors, scale = c(10, 0.1), random.color = TRUE,
ordered.colors = FALSE, vfont = c("serif", "plain")
)## Warning in wordcloud::wordcloud(names(topicfreq), sqrt(topicfreq), min.freq =
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Citation analysis
Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.
Get citing papers for each submission
citations <- citecorp::oc_coci_cites(doi = papers$alternative.id) %>%
dplyr::distinct()
dim(citations)## [1] 3261 7citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci))
tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
contains("type"), contains("publisher"), contains("prefix"))
citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume
## that they can be
## identified from the prefix 10.1101 - set the container.title
## for these records manually; we may or may not want to count these
## (would it count citations twice, both preprint and publication?)
citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
## JOSS is represented by 'The Journal of Open Source Software' as well as
## 'Journal of Open Source Software'
citations$container.title[citations$container.title ==
"Journal of Open Source Software"] <-
"The Journal of Open Source Software"
## Remove real self citations (cited DOI = citing DOI)
citations <- citations %>% dplyr::filter(cited != citing)
## Merge with the archive
citations <- dplyr::bind_rows(citations, citations_archive)
write.table(citations, file = "joss_submission_citations.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)Summary statistics
## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 431## Number of JOSS papers with >0 citations according to Crossref
length(which(papers$citation_count > 0))## [1] 552## Number of citations from Open Citations Corpus vs Crossref
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## [1] 4959## [1] 3247## Ratio of total citation count Open Citations Corpus/Crossref
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 0.6547691ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()
## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))
## [1] 1024## [1] 852Most citing journals
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)Save object
The tibble object with all data collected above is serialized to a file that can be downloaded and reused.
## alternative.id container.title created deposited
## 1 10.21105/joss.00900 Journal of Open Source Software 2018-09-23 2018-09-23
## 2 10.21105/joss.00236 The Journal of Open Source Software 2017-04-19 2019-09-21
## 3 10.21105/joss.02581 Journal of Open Source Software 2020-08-26 2020-08-26
## 4 10.21105/joss.02520 Journal of Open Source Software 2020-08-26 2020-08-26
## 5 10.21105/joss.01423 Journal of Open Source Software 2019-05-08 2019-11-17
## 6 10.21105/joss.01614 Journal of Open Source Software 2019-08-20 2019-11-17
## published.print doi indexed issn issue issued
## 1 2018-09-23 10.21105/joss.00900 2020-03-10 2475-9066 29 2018-09-23
## 2 2017-04-19 10.21105/joss.00236 2020-08-26 2475-9066 12 2017-04-19
## 3 2020-08-26 10.21105/joss.02581 2020-08-26 2475-9066 52 2020-08-26
## 4 2020-08-26 10.21105/joss.02520 2020-08-26 2475-9066 52 2020-08-26
## 5 2019-05-08 10.21105/joss.01423 2020-04-07 2475-9066 37 2019-05-08
## 6 2019-08-20 10.21105/joss.01614 2020-02-14 2475-9066 40 2019-08-20
## member page prefix publisher reference.count score source
## 1 8722 900 10.21105 The Open Journal 9 1 Crossref
## 2 8722 236 10.21105 The Open Journal 12 1 Crossref
## 3 8722 2581 10.21105 The Open Journal 10 1 Crossref
## 4 8722 2520 10.21105 The Open Journal 11 1 Crossref
## 5 8722 1423 10.21105 The Open Journal 9 1 Crossref
## 6 8722 1614 10.21105 The Open Journal 7 1 Crossref
## title
## 1 GB code: A grain boundary generation code
## 2 Brightway: An open source framework for Life Cycle Assessment
## 3 SALSA: A Python Package for Constructing Synthetic Quasar Absorption Line Catalogs from Astrophysical Hydrodynamic Simulations
## 4 Gridap: An extensible Finite Element toolbox in Julia
## 5 CRED: a rapid peak caller for Chem-seq data
## 6 drms: A Python package for accessing HMI and AIA data
## type url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.00900 3
## 2 journal-article http://dx.doi.org/10.21105/joss.00236 2
## 3 journal-article http://dx.doi.org/10.21105/joss.02581 5
## 4 journal-article http://dx.doi.org/10.21105/joss.02520 5
## 5 journal-article http://dx.doi.org/10.21105/joss.01423 4
## 6 journal-article http://dx.doi.org/10.21105/joss.01614 4
## author
## 1 http://orcid.org/0000-0002-9616-4602, http://orcid.org/0000-0003-4281-5665, NA, FALSE, FALSE, NA, R., B., J., Hadian, Grabowski, Neugebauer, first, additional, additional
## 2 http://orcid.org/0000-0002-7898-9862, FALSE, Chris, Mutel, first
## 3 http://orcid.org/0000-0003-0872-7098, NA, NA, NA, NA, NA, FALSE, NA, NA, NA, NA, NA, Brendan, Devin, Brian, Jason, Molly, Nicholas, Boyd, Silvia, O’Shea, Tumlinson, Peeples, Earl, first, additional, additional, additional, additional, additional
## 4 http://orcid.org/0000-0003-2391-4086, http://orcid.org/0000-0003-3667-443X, FALSE, FALSE, Santiago, Francesc, Badia, Verdugo, first, additional
## 5 http://orcid.org/0000-0002-8086-3185, http://orcid.org/0000-0003-2358-7919, http://orcid.org/0000-0002-0916-7339, http://orcid.org/0000-0002-3992-5399, FALSE, FALSE, FALSE, FALSE, Jason, Tony, Paul, Hiroki, Lin, Kuo, Horton, Nagase, first, additional, additional, additional
## 6 http://orcid.org/0000-0002-1361-5712, http://orcid.org/0000-0002-5662-9604, http://orcid.org/0000-0001-6915-4583, http://orcid.org/0000-0002-0361-6463, http://orcid.org/0000-0003-4217-4642, FALSE, FALSE, FALSE, FALSE, FALSE, Kolja, Monica, Nitin, Arthur, Stuart, Glogowski, Bobra, Choudhary, Amezcua, Mumford, first, additional, additional, additional, additional
## citation_count
## 1 1
## 2 39
## 3 0
## 4 0
## 5 1
## 6 2
## api_title
## 1 GB code: A grain boundary generation code
## 2 Brightway: An open source framework for Life Cycle Assessment
## 3 SALSA: A Python Package for Constructing Synthetic Quasar Absorption Line Catalogs from Astrophysical Hydrodynamic Simulations
## 4 Gridap: An extensible Finite Element toolbox in Julia
## 5 CRED: a rapid peak caller for Chem-seq data
## 6 drms: A Python package for accessing HMI and AIA data
## api_state editor reviewers
## 1 accepted @labarba @vyasr,@trallard
## 2 accepted @katyhuff @amoeba
## 3 accepted @danielskatz @olebole,@zpace
## 4 accepted @Kevin-Mattheus-Moerman @PetrKryslUCSD,@TeroFrondelius
## 5 accepted @lpantano @darogan
## 6 accepted @xuanxu @mgckind,@aureliocarnero
## nbr_reviewers repo_url review_issue_id
## 1 2 https://github.com/oekosheri/GB_code 900
## 2 1 https://bitbucket.org/cmutel/brightway2 236
## 3 2 https://github.com/biboyd/SALSA 2581
## 4 2 https://github.com/gridap/Gridap.jl 2520
## 5 1 https://github.com/jlincbio/cred 1423
## 6 2 https://github.com/sunpy/drms 1614
## prereview_issue_id languages
## 1 853 Python,TeX
## 2 228 Python,Shell,Jupyter Notebook,HTML,TeX
## 3 2532 Jupyter Notebook,TeX,Shell,Python
## 4 2464 Julia,Shell,TeX
## 5 1374 Makefile,Perl,C,TeX
## 6 1559 Python,TeX
## archive_doi
## 1 https://doi.org/10.5281/zenodo.1433530
## 2 http://dx.doi.org/10.5281/zenodo.556145
## 3 https://doi.org/10.5281/zenodo.4002067
## 4 https://doi.org/10.5281/zenodo.3999839
## 5 https://doi.org/10.5281/zenodo.2667613
## 6 https://doi.org/10.5281/zenodo.3369966
## review_title
## 1 GB_code: A grain boundary generation code
## 2 Brightway: An open source framework for life cycle assessment
## 3 SALSA: A Python Package for Constructing Synthetic Quasar Absorption Line Catalogs from Astrophysical Hydrodynamic Simulations
## 4 Gridap: An extensible Finite Element toolbox in Julia
## 5 CRED: a rapid peak caller for Chem-seq data
## 6 drms: A Python package for accessing HMI and AIA data
## review_number review_state review_opened review_closed review_ncomments
## 1 900 closed 2018-08-17 2018-09-23 90
## 2 236 closed 2017-04-13 2017-04-19 18
## 3 2581 closed 2020-08-18 2020-08-26 38
## 4 2520 closed 2020-07-26 2020-08-26 74
## 5 1423 closed 2019-05-01 2019-05-08 93
## 6 1614 closed 2019-08-01 2019-08-20 62
## review_labels
## 1 accepted,published,recommend-accept
## 2 accepted,published,recommend-accept
## 3 Jupyter Notebook,Shell,TeX,accepted,published,recommend-accept
## 4 Julia,TeX,accepted,published,recommend-accept
## 5 accepted,published,recommend-accept
## 6 accepted,published,recommend-accept
## prerev_title
## 1 GB_code: A grain boundary generation code
## 2 Brightway: An open source framework for life cycle assessment
## 3 SALSA: A Python Package for Constructing Synthetic Quasar Absorption Line Catalogs from Astrophysical Hydrodynamic Simulations
## 4 Gridap: An extensible Finite Element toolbox in Julia
## 5 CRED: a rapid peak caller for Chem-seq data
## 6 drms: A Python package for accessing HMI and AIA data
## prerev_state prerev_opened prerev_closed prerev_ncomments
## 1 closed 2018-07-26 2018-08-17 42
## 2 closed 2017-04-06 2017-04-13 14
## 3 closed 2020-07-28 2020-08-18 44
## 4 closed 2020-07-10 2020-07-26 47
## 5 closed 2019-04-14 2019-05-01 30
## 6 closed 2019-07-11 2019-08-01 29
## prerev_labels days_in_pre days_in_rev to_review repo_created
## 1 Python,TeX 22 days 37 days TRUE 2018-07-12
## 2 7 days 6 days TRUE <NA>
## 3 Jupyter Notebook,Shell,TeX 21 days 8 days TRUE 2020-06-11
## 4 Julia,TeX 16 days 31 days TRUE 2019-03-15
## 5 C,Makefile,Perl 17 days 7 days TRUE 2019-04-10
## 6 Python 21 days 19 days TRUE 2016-05-12
## repo_updated repo_pushed repo_nbr_stars repo_language
## 1 2020-09-23 2019-08-08 16 Python
## 2 <NA> <NA> NA <NA>
## 3 2020-08-27 2020-08-27 2 Python
## 4 2020-09-21 2020-09-24 112 Julia
## 5 2020-02-26 2020-02-26 1 C
## 6 2020-04-16 2020-04-16 11 Python
## repo_languages_bytes
## 1 Python:47666,TeX:2712
## 2 <NA>
## 3 Python:97186,Jupyter Notebook:21308,TeX:8559,Shell:546
## 4 Julia:1093861
## 5 C:30602,Perl:5262,TeX:3999,Makefile:960
## 6 Python:180067,TeX:7895
## repo_topics
## 1 linear-algebra,crystallography,high-throughput-computing,grain-boundaries,python
## 2 <NA>
## 3
## 4 julia,pdes,partial-differential-equations,finite-elements,numerical-methods,gridap
## 5
## 6
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 mit 2 2 2020-09-27
## 2 <NA> NA NA <NA>
## 3 bsd-3-clause 2 1 2020-09-27
## 4 mit 11 8 2020-09-27
## 5 gpl-3.0 2 1 2020-09-27
## 6 mit 7 5 2020-09-27
## published.date halfyear nbr_authors
## 1 2018-09-23 2018H2 3
## 2 2017-04-19 2017H1 1
## 3 2020-08-26 2020H2 6
## 4 2020-08-26 2020H2 2
## 5 2019-05-08 2019H1 4
## 6 2019-08-20 2019H2 5To read the current version of this file directly from GitHub, use the following code:
Session info
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readr_1.3.1 citecorp_0.3.0 plotly_4.9.2.1 DT_0.15
## [5] jsonlite_1.7.1 purrr_0.3.4 gh_1.1.0 lubridate_1.7.9
## [9] ggplot2_3.3.2 tidyr_1.1.2 dplyr_1.0.2 rcrossref_1.0.0
## [13] tibble_3.0.3
##
## loaded via a namespace (and not attached):
## [1] viridis_0.5.1 httr_1.4.2 viridisLite_0.3.0 splines_4.0.2
## [5] shiny_1.5.0 assertthat_0.2.1 triebeard_0.3.0 urltools_1.7.3
## [9] yaml_2.2.1 pillar_1.4.6 lattice_0.20-41 glue_1.4.2
## [13] digest_0.6.25 RColorBrewer_1.1-2 promises_1.1.1 colorspace_1.4-1
## [17] Matrix_1.2-18 htmltools_0.5.0 httpuv_1.5.4 plyr_1.8.6
## [21] pkgconfig_2.0.3 bibtex_0.4.2.3 httpcode_0.3.0 xtable_1.8-4
## [25] scales_1.1.1 whisker_0.4 later_1.1.0.1 mgcv_1.8-31
## [29] generics_0.0.2 farver_2.0.3 ellipsis_0.3.1 withr_2.3.0
## [33] lazyeval_0.2.2 cli_2.0.2 magrittr_1.5 crayon_1.3.4
## [37] mime_0.9 evaluate_0.14 fansi_0.4.1 nlme_3.1-148
## [41] xml2_1.3.2 tools_4.0.2 data.table_1.13.0 hms_0.5.3
## [45] lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0 compiler_4.0.2
## [49] rlang_0.4.7 grid_4.0.2 rstudioapi_0.11 htmlwidgets_1.5.1
## [53] crosstalk_1.1.0.1 miniUI_0.1.1.1 labeling_0.3 rmarkdown_2.3
## [57] gtable_0.3.0 curl_4.3 fauxpas_0.5.0 R6_2.4.1
## [61] gridExtra_2.3 knitr_1.30 fastmap_1.0.1 utf8_1.1.4
## [65] stringi_1.5.3 crul_1.0.0 Rcpp_1.0.5 vctrs_0.3.4
## [69] wordcloud_2.6 tidyselect_1.1.0 xfun_0.17